PROTAC-Degradation-Predictor

A machine learning-based tool for predicting PROTAC protein degradation activity.

📚 Table of Contents

• Installation
• Documentation and Usage
• Data Curation
• Training
• Citation
• License

🚀 Installation

The package has been developed and tested on a Linux machine with Python 3.12.4. It is recommended to use a virtual environment to avoid conflicts with other packages.

To install the package, open your terminal and please run the following commands:

git clone --branch=main --depth=1 https://github.com/ribesstefano/PROTAC-Degradation-Predictor.git
cd PROTAC-Degradation-Predictor
python -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -e .

🎯 Documentation and Usage

The package documentation can be found here.

After installing the package, you can use it as follows:

import protac_degradation_predictor as pdp

protac_smiles = 'Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O)[C@@H](NC(=O)COCCCCCCCCCOCC(=O)Nc2ccc(C(=O)Nc3ccc(F)cc3N)cc2)C(C)(C)C)cc1'
e3_ligase = 'VHL'
target_uniprot = 'P04637'
cell_line = 'HeLa'

active_protac = pdp.is_protac_active(
    protac_smiles,
    e3_ligase,
    target_uniprot,
    cell_line,
)

print(f'The given PROTAC is: {"active" if active_protac else "inactive"}')

This example demonstrates how to predict the activity of a PROTAC molecule. The is_protac_active function takes the SMILES string of the PROTAC, the E3 ligase, the UniProt ID of the target protein, and the cell line as inputs. It returns whether the PROTAC is active or not.

The function supports batch computation by passing lists of SMILES strings, E3 ligases, UniProt IDs, and cell lines. In this case, it returns a list of booleans indicating the activity of each PROTAC.

📝 Data Curation

The code for data curation can be found in the Jupyter notebook data_curation.ipynb.

📈 Training

Before running the experiments reported in our work or train on your custom dataset, here are some required steps to follow (assuming one is in the repository directory already):

1. Download the data from the Cellosaurus database and save it in the data directory:

wget https://ftp.expasy.org/databases/cellosaurus/cellosaurus.txt data/

2. Make a copy of the Uniprot embeddings to be placed in the data directory:

cp protac_degradation_predictor/data/uniprot2embedding.h5 data/

3. Create a virtual environment and install the required packages by running the following commands:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install --upgrade pip
pip install -r requirements.txt
pip install -r scripts/requirements.txt

4. The code for training the PyTorch models can be found in the file run_experiments_pytorch.py.

(Don't forget to adjust the PYTHONPATH environment variable to include the repository directory: export PYTHONPATH=$PYTHONPATH:/path/to/PROTAC-Degradation-Predictor)

Training on Custom Dataset

For training a model on a user-provided dataset, please refer to the guide reported in this README.

📄 Citation

If you use this tool in your research, please cite the following paper:

@article{Ribes_2024,
   title={Modeling PROTAC degradation activity with machine learning},
   volume={6},
   ISSN={2667-3185},
   url={http://dx.doi.org/10.1016/j.ailsci.2024.100104},
   DOI={10.1016/j.ailsci.2024.100104},
   journal={Artificial Intelligence in the Life Sciences},
   publisher={Elsevier BV},
   author={Ribes, Stefano and Nittinger, Eva and Tyrchan, Christian and Mercado, Rocío},
   year={2024},
   month=dec, pages={100104}
}

The directories logs and reports contain the logs and reports generated during the experiments reported in the paper. Additionally, in reports, one can find the pickled Optuna studies for the reported experiments.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.